The enduring art of problem finding

The arrival of sophisticated AI tooling has transformed how organizations think about their operational challenges. Where once we saw rigid workflows and immovable bottlenecks, we now see layers of abstraction waiting to be automated. Tasks that consumed countless human hours can be delegated to systems that never tire or lose focus. This revolution promises to unlock human creativity by removing mundane work from our plates. Yet for all this technological promise, we often discover that eliminating one constraint merely illuminates another. The bottleneck shifts rather than disappears. Success lies not in the raw application of new tools but in understanding which problems deserve our attention.

The evolution of chess computers offers a perfect parable for this dynamic. Early programs in the 1970s and 1980s attempted to encode human expertise directly into silicon. Programmers painstakingly translated opening theory and positional understanding into evaluation functions. These machines played competent chess but remained vulnerable to human intuition. Then came Deep Blue, which could evaluate 200 million chess positions per second when it defeated Garry Kasparov in 1997. This wasn't merely brute force triumphing over finesse. The real breakthrough came from marrying computational power with more efficient search algorithms. The team had doubled the speed of the system and increased the chess knowledge by adding features to the chess chip that enabled it to recognize different positions. Years later, AlphaZero would take this further by using self-play and neural networks to discover chess principles from scratch. Each leap forward combined raw compute with algorithmic innovation. The lesson wasn't that compute beats cleverness but that cleverness makes compute effective.

This brings us to a remarkable development in artificial reasoning. The Hierarchical Reasoning Model represents a fundamentally different approach to machine intelligence. Rather than pursuing ever-larger models that demand massive datasets and computational resources, HRM takes inspiration from the human brain's hierarchical processing. The architecture employs two interconnected modules operating at different timescales. A high-level module handles abstract planning while a low-level module executes rapid detailed computations. This design allows the model to achieve extraordinary depth without the training instability that plagues other approaches. The results speak to the power of this clever reformulation.

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What makes HRM compelling is its efficiency. With only 27 million parameters and trained on merely 1000 examples, it achieves nearly perfect accuracy on complex Sudoku puzzles and optimal pathfinding in large mazes. These are tasks where state-of-the-art models with billions of parameters fail completely despite their vast training data. On ARC-AGI 1 HRM scores 40.3, substantially outperforming models thousands of times larger. This isn't a marginal improvement it's a fundamental rethinking of how machines might reason. The model discovers algorithms for solving diverse problems without being explicitly programmed for any of them individually. Now time will tell whether this elegant architecture can scale to achieve new monstrous utility.  

The deeper lesson transcends any particular technology. Organizations rush to adopt powerful new tools, believing that computational muscle will solve their problems. Yet the chess story reminds us that raw power without strategic application achieves little. Deep Blue didn't just calculate faster than Kasparov. It combined speed with sophisticated evaluation functions refined by grandmasters. Similarly, HRM doesn't just process information differently. It embodies a theory about how reasoning actually works. The organizations that thrive won't be those with the biggest computational budgets but those that identify which problems truly matter and frame them in ways that make solutions tractable. The skill that endures through every technological revolution is the ability to see past the tool to the problem it should solve. In a world where compute grows cheaper and algorithms grow cleverer, the scarcest resource remains human insight into what questions are worth asking.

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